




'''simple docstring'''



import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


UpperCamelCase_    	=				getLogger(__name__)


UpperCamelCase_    	=				"cuda" if torch.cuda.is_available() else "cpu"




def   lowercase__(			__UpperCamelCase:					List[str]      ,__UpperCamelCase:					str      ,__UpperCamelCase:					str      ,__UpperCamelCase:					int = 8      ,__UpperCamelCase:					str = DEFAULT_DEVICE      ,__UpperCamelCase:					Optional[int]=False      ,__UpperCamelCase:					Tuple="summarization"      ,__UpperCamelCase:					Tuple=None      ,**__UpperCamelCase:					List[str]      ,):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      List[str]    					=						Path(__UpperCamelCase    ).open('w'      ,encoding='utf-8'    )
      SCREAMING_SNAKE_CASE   :      int    					=						str(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      int    					=						AutoModelForSeqaSeqLM.from_pretrained(__UpperCamelCase    ).to(__UpperCamelCase    )
      if fpaa:
            SCREAMING_SNAKE_CASE   :      Any    					=						model.half()

      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						AutoTokenizer.from_pretrained(__UpperCamelCase    )
      logger.info(f"Inferred tokenizer type: {tokenizer.__class__}"    )  # if this is wrong, check config.model_type.

      SCREAMING_SNAKE_CASE   :      Dict    					=						time.time()
      # update config with task specific params
      use_task_specific_params(__UpperCamelCase      ,__UpperCamelCase    )
      if prefix is None:
            SCREAMING_SNAKE_CASE   :      int    					=						prefix or getattr(model.config      ,'prefix'      ,''    ) or ''
      for examples_chunk in tqdm(list(chunks(__UpperCamelCase      ,__UpperCamelCase    )    )    ):
            SCREAMING_SNAKE_CASE   :      Any    					=						[prefix + text for text in examples_chunk]
            SCREAMING_SNAKE_CASE   :      Tuple    					=						tokenizer(__UpperCamelCase      ,return_tensors='pt'      ,truncation=__UpperCamelCase      ,padding='longest'    ).to(__UpperCamelCase    )
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						model.generate(
                input_ids=batch.input_ids      ,attention_mask=batch.attention_mask      ,**__UpperCamelCase      ,)
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer.batch_decode(__UpperCamelCase      ,skip_special_tokens=__UpperCamelCase      ,clean_up_tokenization_spaces=__UpperCamelCase    )
            for hypothesis in dec:
                  fout.write(hypothesis + '\n'    )
                  fout.flush()
      fout.close()
      SCREAMING_SNAKE_CASE   :      Tuple    					=						int(time.time() - start_time    )  # seconds
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						len(__UpperCamelCase    )
      return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs      ,4    )}




def   lowercase__(			):

      """simple docstring"""






      return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'    )




def   lowercase__(			__UpperCamelCase:					List[str]=True    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      int    					=						argparse.ArgumentParser()
      parser.add_argument('model_name'      ,type=__UpperCamelCase      ,help='like facebook/bart-large-cnn,t5-base, etc.'    )
      parser.add_argument('input_path'      ,type=__UpperCamelCase      ,help='like cnn_dm/test.source'    )
      parser.add_argument('save_path'      ,type=__UpperCamelCase      ,help='where to save summaries'    )
      parser.add_argument('--reference_path'      ,type=__UpperCamelCase      ,required=__UpperCamelCase      ,help='like cnn_dm/test.target'    )
      parser.add_argument('--score_path'      ,type=__UpperCamelCase      ,required=__UpperCamelCase      ,default='metrics.json'      ,help='where to save metrics'    )
      parser.add_argument('--device'      ,type=__UpperCamelCase      ,required=__UpperCamelCase      ,default=__UpperCamelCase      ,help='cuda, cuda:1, cpu etc.'    )
      parser.add_argument(
          '--prefix'      ,type=__UpperCamelCase      ,required=__UpperCamelCase      ,default=__UpperCamelCase      ,help='will be added to the begininng of src examples'    )
      parser.add_argument('--task'      ,type=__UpperCamelCase      ,default='summarization'      ,help='used for task_specific_params + metrics'    )
      parser.add_argument('--bs'      ,type=__UpperCamelCase      ,default=8      ,required=__UpperCamelCase      ,help='batch size'    )
      parser.add_argument(
          '--n_obs'      ,type=__UpperCamelCase      ,default=-1      ,required=__UpperCamelCase      ,help='How many observations. Defaults to all.'    )
      parser.add_argument('--fp16'      ,action='store_true'    )
      parser.add_argument('--dump-args'      ,action='store_true'      ,help='print the custom hparams with the results'    )
      parser.add_argument(
          '--info'      ,nargs='?'      ,type=__UpperCamelCase      ,const=datetime_now()      ,help=(
              'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
              ' lang=en-ru. If no value is passed, the current datetime string will be used.'
          )      ,)
      # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						parser.parse_known_args()
      SCREAMING_SNAKE_CASE   :      int    					=						parse_numeric_n_bool_cl_kwargs(__UpperCamelCase    )
      if parsed_args and verbose:
            print(f"parsed the following generate kwargs: {parsed_args}"    )
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path    ).readlines()]
      if args.n_obs > 0:
            SCREAMING_SNAKE_CASE   :      str    					=						examples[: args.n_obs]
      Path(args.save_path    ).parent.mkdir(exist_ok=__UpperCamelCase    )

      if args.reference_path is None and Path(args.score_path    ).exists():
            warnings.warn(f"score_path {args.score_path} will be overwritten unless you type ctrl-c."    )

      if args.device == "cpu" and args.fpaa:
            # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
            raise ValueError('Can\'t mix --fp16 and --device cpu'    )

      SCREAMING_SNAKE_CASE   :      int    					=						generate_summaries_or_translations(
          __UpperCamelCase      ,args.save_path      ,args.model_name      ,batch_size=args.bs      ,device=args.device      ,fpaa=args.fpaa      ,task=args.task      ,prefix=args.prefix      ,**__UpperCamelCase      ,)

      if args.reference_path is None:
            return {}

      # Compute scores
      SCREAMING_SNAKE_CASE   :      Tuple    					=						calculate_bleu if 'translation' in args.task else calculate_rouge
      SCREAMING_SNAKE_CASE   :      int    					=						[x.rstrip() for x in open(args.save_path    ).readlines()]
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[x.rstrip() for x in open(args.reference_path    ).readlines()][: len(__UpperCamelCase    )]
      SCREAMING_SNAKE_CASE   :      dict    					=						score_fn(__UpperCamelCase      ,__UpperCamelCase    )
      scores.update(__UpperCamelCase    )

      if args.dump_args:
            scores.update(__UpperCamelCase    )
      if args.info:
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						args.info

      if verbose:
            print(__UpperCamelCase    )

      if args.score_path is not None:
            json.dump(__UpperCamelCase      ,open(args.score_path      ,'w'    )    )

      return scores


if __name__ == "__main__":
      # Usage for MT:
      # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
      run_generate(verbose=True)










'''simple docstring'''





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class  snake_case__      (					UpperCamelCase):
					a_        =					["image_processor", "tokenizer"]
					a_        =					"LayoutLMv2ImageProcessor"
					a_        =					("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")




					def __init__(  self  :		Optional[int]				, _A  :		str=None				, _A  :		Optional[Any]=None				, **_A  :		Any      )    ->							Tuple:
								if "feature_extractor" in kwargs:
											warnings.warn(
											    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
											    ''' instead.'''				, _A				, )
											UpperCAmelCase_      :       int          =       kwargs.pop('''feature_extractor'''      )

								UpperCAmelCase_      :       str          =       image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
											raise ValueError('''You need to specify an `image_processor`.'''      )
								if tokenizer is None:
											raise ValueError('''You need to specify a `tokenizer`.'''      )

								super().__init__(_A				, _A      )




					def __call__(  self  :		str				, _A  :		Optional[int]				, _A  :		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				, _A  :		Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None				, _A  :		Union[List[List[int]], List[List[List[int]]]] = None				, _A  :		Optional[Union[List[int], List[List[int]]]] = None				, _A  :		bool = True				, _A  :		Union[bool, str, PaddingStrategy] = False				, _A  :		Union[bool, str, TruncationStrategy] = None				, _A  :		Optional[int] = None				, _A  :		int = 0				, _A  :		Optional[int] = None				, _A  :		Optional[bool] = None				, _A  :		Optional[bool] = None				, _A  :		bool = False				, _A  :		bool = False				, _A  :		bool = False				, _A  :		bool = False				, _A  :		bool = True				, _A  :		Optional[Union[str, TensorType]] = None				, **_A  :		Dict				, )    ->							BatchEncoding:
								# verify input
								if self.image_processor.apply_ocr and (boxes is not None):
											raise ValueError(
											    '''You cannot provide bounding boxes '''
											    '''if you initialized the image processor with apply_ocr set to True.'''      )

								if self.image_processor.apply_ocr and (word_labels is not None):
											raise ValueError(
											    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''      )

								if return_overflowing_tokens is True and return_offsets_mapping is False:
											raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''      )

								# first, apply the image processor
								UpperCAmelCase_      :       int          =       self.image_processor(images=_A				, return_tensors=_A      )

								# second, apply the tokenizer
								if text is not None and self.image_processor.apply_ocr and text_pair is None:
											if isinstance(_A				, _A      ):
														UpperCAmelCase_      :       int          =       [text]  # add batch dimension (as the image processor always adds a batch dimension)
											UpperCAmelCase_      :       int          =       features['''words''']

								UpperCAmelCase_      :       str          =       self.tokenizer(
								    text=text if text is not None else features['''words''']				, text_pair=text_pair if text_pair is not None else None				, boxes=boxes if boxes is not None else features['''boxes''']				, word_labels=_A				, add_special_tokens=_A				, padding=_A				, truncation=_A				, max_length=_A				, stride=_A				, pad_to_multiple_of=_A				, return_token_type_ids=_A				, return_attention_mask=_A				, return_overflowing_tokens=_A				, return_special_tokens_mask=_A				, return_offsets_mapping=_A				, return_length=_A				, verbose=_A				, return_tensors=_A				, **_A				, )

								# add pixel values
								UpperCAmelCase_      :       int          =       features.pop('''pixel_values'''      )
								if return_overflowing_tokens is True:
											UpperCAmelCase_      :       List[Any]          =       self.get_overflowing_images(_A				, encoded_inputs['''overflow_to_sample_mapping''']      )
								UpperCAmelCase_      :       Optional[int]          =       images

								return encoded_inputs




					def 		A      (  self  :		Union[str, Any]				, _A  :		int				, _A  :		Tuple      )    ->							Dict:
								# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
								UpperCAmelCase_      :       Tuple          =       []
								for sample_idx in overflow_to_sample_mapping:
											images_with_overflow.append(images[sample_idx]      )

								if len(_A      ) != len(_A      ):
											raise ValueError(
											    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
											    F" {len(_A      )} and {len(_A      )}"      )

								return images_with_overflow




					def 		A      (  self  :		Optional[Any]				, *_A  :		Union[str, Any]				, **_A  :		Union[str, Any]      )    ->							Tuple:
								return self.tokenizer.batch_decode(*_A				, **_A      )




					def 		A      (  self  :		Any				, *_A  :		Optional[Any]				, **_A  :		Tuple      )    ->							Tuple:
								return self.tokenizer.decode(*_A				, **_A      )




					@property
					def 		A      (  self  :		Union[str, Any]      )    ->							List[Any]:
								return ["input_ids", "bbox", "attention_mask", "image"]




					@property
					def 		A      (  self  :		Tuple      )    ->							Optional[int]:
								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				, _A				, )
								return self.image_processor_class





					@property
					def 		A      (  self  :		Tuple      )    ->							str:
								warnings.warn(
								    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				, _A				, )
								return self.image_processor


